## pval_cutoff: 0.05
## lfc_cutoff: 1
## low_counts_cutoff: 10
General statistics
# Number of samples
length(counts_data)
## [1] 6
# Number of genes
nrow(counts_data)
## [1] 49315
# Total counts
colSums(counts_data)
## SRR13535276 SRR13535278 SRR13535280 SRR13535288 SRR13535290 SRR13535292
## 5442647 5284506 6692745 11361755 11378178 4974222
Create DDS objects
# Create DESeqDataSet object
dds <- get_DESeqDataSet_obj(counts_data, ~ treatment)
## [1] TRUE
## [1] TRUE
## [1] "DESeqDataSet object of length 49315 with 0 metadata columns"
## [1] "DESeqDataSet object of length 14816 with 0 metadata columns"
colData(dds)
## DataFrame with 6 rows and 25 columns
## Assay Type AvgSpotLen Bases BioProject BioSample Bytes Center Name Consent DATASTORE filetype DATASTORE provider DATASTORE region Experiment treatment GEO_Accession (exp) Instrument LibraryLayout LibrarySelection LibrarySource Organism Platform label ReleaseDate Sample Name source_name SRA Study
## <character> <numeric> <numeric> <character> <character> <numeric> <character> <character> <character> <character> <character> <character> <factor> <character> <character> <character> <character> <character> <character> <character> <factor> <POSIXct> <character> <character> <character>
## SRR13535276 RNA-Seq 300 8225466000 PRJNA694971 SAMN17588686 3252113587 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943360 A GSM5043430 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043430 C2C12 proliferating .. SRP303354
## SRR13535278 RNA-Seq 300 9203426700 PRJNA694971 SAMN17588684 3619152333 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943362 A GSM5043433 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043433 C2C12 proliferating .. SRP303354
## SRR13535280 RNA-Seq 300 9323939700 PRJNA694971 SAMN17588682 3735905901 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943364 A GSM5043436 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043436 C2C12 proliferating .. SRP303354
## SRR13535288 RNA-Seq 300 12863728500 PRJNA694971 SAMN17587373 5128876770 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943372 C GSM5043450 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space with gravity 2021-09-09 GSM5043450 C2C12 proliferating .. SRP303354
## SRR13535290 RNA-Seq 300 12849825300 PRJNA694971 SAMN17587371 5136077921 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943374 C GSM5043454 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space with gravity 2021-09-09 GSM5043454 C2C12 proliferating .. SRP303354
## SRR13535292 RNA-Seq 300 10569142200 PRJNA694971 SAMN17587369 4229018065 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943376 C GSM5043457 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space with gravity 2021-09-09 GSM5043457 C2C12 proliferating .. SRP303354
Sample-to-sample comparisons
# Transform data (blinded rlog)
rld <- get_transformed_data(dds)
PCA plot
pca <- rld$pca
pca_df <- cbind(as.data.frame(colData(dds)) %>% rownames_to_column(var = 'name'), pca$x)
summary(pca)
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6
## Standard deviation 32.1475 31.2210 27.8763 24.7058 21.724 4.987e-14
## Proportion of Variance 0.2672 0.2520 0.2009 0.1578 0.122 0.000e+00
## Cumulative Proportion 0.2672 0.5192 0.7202 0.8780 1.000 1.000e+00
ggplot(pca_df, aes(x = PC1, y = PC2, color = label)) +
geom_point() +
geom_text(aes(label = name), position = position_nudge(y = -2), show.legend = F, size = 3) +
scale_color_manual(values = colors_default) +
scale_x_continuous(expand = c(0.2, 0))
Correlation heatmap
pheatmap(
cor(rld$matrix),
annotation_col = as.data.frame(colData(dds)) %>% select(label),
color = brewer.pal(8, 'YlOrRd')
)
Wald test results
# DE analysis using Wald test
dds_full <- DESeq(dds)
colData(dds_full)
## DataFrame with 6 rows and 26 columns
## Assay Type AvgSpotLen Bases BioProject BioSample Bytes Center Name Consent DATASTORE filetype DATASTORE provider DATASTORE region Experiment treatment GEO_Accession (exp) Instrument LibraryLayout LibrarySelection LibrarySource Organism Platform label ReleaseDate Sample Name source_name SRA Study sizeFactor
## <character> <numeric> <numeric> <character> <character> <numeric> <character> <character> <character> <character> <character> <character> <factor> <character> <character> <character> <character> <character> <character> <character> <factor> <POSIXct> <character> <character> <character> <numeric>
## SRR13535276 RNA-Seq 300 8225466000 PRJNA694971 SAMN17588686 3252113587 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943360 A GSM5043430 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043430 C2C12 proliferating .. SRP303354 0.667323
## SRR13535278 RNA-Seq 300 9203426700 PRJNA694971 SAMN17588684 3619152333 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943362 A GSM5043433 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043433 C2C12 proliferating .. SRP303354 0.816941
## SRR13535280 RNA-Seq 300 9323939700 PRJNA694971 SAMN17588682 3735905901 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943364 A GSM5043436 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043436 C2C12 proliferating .. SRP303354 0.752370
## SRR13535288 RNA-Seq 300 12863728500 PRJNA694971 SAMN17587373 5128876770 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943372 C GSM5043450 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space with gravity 2021-09-09 GSM5043450 C2C12 proliferating .. SRP303354 2.071709
## SRR13535290 RNA-Seq 300 12849825300 PRJNA694971 SAMN17587371 5136077921 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943374 C GSM5043454 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space with gravity 2021-09-09 GSM5043454 C2C12 proliferating .. SRP303354 1.547156
## SRR13535292 RNA-Seq 300 10569142200 PRJNA694971 SAMN17587369 4229018065 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943376 C GSM5043457 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space with gravity 2021-09-09 GSM5043457 C2C12 proliferating .. SRP303354 0.793327
# Wald test results
res <- results(
dds_full,
contrast = c('treatment', condition, control),
alpha = pval_cutoff
)
res
## log2 fold change (MLE): treatment A vs C
## Wald test p-value: treatment A vs C
## DataFrame with 14816 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000098104 6.35502 0.7602418 1.277274 0.5952064 0.551705 0.916505
## ENSMUSG00000103922 3.00821 -0.4331325 1.505083 -0.2877798 0.773515 NA
## ENSMUSG00000033845 127.62317 -0.3888411 0.565087 -0.6881089 0.491384 0.909368
## ENSMUSG00000102275 2.53432 0.0521924 1.489729 0.0350348 0.972052 NA
## ENSMUSG00000025903 96.02443 -0.2286828 0.361327 -0.6328963 0.526801 0.913742
## ... ... ... ... ... ... ...
## ENSMUSG00000061654 74.15062 -5.8890703 3.776217 -1.5595158 NA NA
## ENSMUSG00000079834 36.06458 -0.2951959 0.577612 -0.5110627 0.60930718 0.926139
## ENSMUSG00000095041 247.75852 -0.0310833 0.702407 -0.0442526 0.96470305 0.996240
## ENSMUSG00000063897 25.05491 0.1056571 0.547329 0.1930412 0.84692669 0.978145
## ENSMUSG00000095742 5.67615 3.3261117 1.190578 2.7936945 0.00521097 0.233620
mcols(res)
## DataFrame with 6 rows and 2 columns
## type description
## <character> <character>
## baseMean intermediate mean of normalized c..
## log2FoldChange results log2 fold change (ML..
## lfcSE results standard error: trea..
## stat results Wald statistic: trea..
## pvalue results Wald test p-value: t..
## padj results BH adjusted p-values
summary(res)
##
## out of 14816 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 7, 0.047%
## LFC < 0 (down) : 60, 0.4%
## outliers [1] : 159, 1.1%
## low counts [2] : 2586, 17%
## (mean count < 5)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
plotDispEsts(dds_full)
Summary details
# Upregulated genes (LFC > 0)
res_sig_df %>% filter(log2FoldChange > 0)
# Downregulated genes (LFC < 0)
res_sig_df %>% filter(log2FoldChange < 0)
# Outliers (pvalue and padj are NA)
res[which(is.na(res$pvalue)), ]
## log2 fold change (MLE): treatment A vs C
## Wald test p-value: treatment A vs C
## DataFrame with 159 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000103509 7.4013 -5.90608 3.35481 -1.76048 NA NA
## ENSMUSG00000079554 27.6269 -6.82795 3.46170 -1.97243 NA NA
## ENSMUSG00000085842 28.7558 4.16157 2.17551 1.91291 NA NA
## ENSMUSG00000103553 11.1104 -5.49768 2.81475 -1.95317 NA NA
## ENSMUSG00000047496 20.2028 -2.07168 1.27171 -1.62905 NA NA
## ... ... ... ... ... ... ...
## ENSMUSG00000036452 108.1271 -4.32528 1.41232 -3.06254 NA NA
## ENSMUSG00000053846 27.1020 -5.89974 2.22511 -2.65144 NA NA
## ENSMUSG00000024867 18.2290 -1.55450 1.46155 -1.06359 NA NA
## ENSMUSG00000048029 22.7289 -6.52465 3.90834 -1.66942 NA NA
## ENSMUSG00000061654 74.1506 -5.88907 3.77622 -1.55952 NA NA
# Low counts (only padj is NA)
res[which(is.na(res$padj) & !is.na(res$pvalue)), ]
## log2 fold change (MLE): treatment A vs C
## Wald test p-value: treatment A vs C
## DataFrame with 2586 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000103922 3.00821 -0.4331325 1.50508 -0.2877798 0.773515 NA
## ENSMUSG00000102275 2.53432 0.0521924 1.48973 0.0350348 0.972052 NA
## ENSMUSG00000103280 2.25921 -0.2036441 1.54506 -0.1318034 0.895140 NA
## ENSMUSG00000103355 3.20134 -0.0484038 1.26490 -0.0382670 0.969475 NA
## ENSMUSG00000103845 2.38594 -1.2816787 1.97493 -0.6489755 0.516354 NA
## ... ... ... ... ... ... ...
## ENSMUSG00000064347 2.33249 2.646153 1.66191 1.592236 0.111332 NA
## ENSMUSG00000064360 3.81827 0.236955 1.17589 0.201512 0.840298 NA
## ENSMUSG00000064364 4.38183 -1.462709 1.21266 -1.206204 0.227739 NA
## ENSMUSG00000064365 2.11846 1.897276 1.77519 1.068774 0.285171 NA
## ENSMUSG00000064366 4.04585 -1.225589 2.08042 -0.589107 0.555790 NA
Shrunken LFC results
plotMA(res)
# Shrunken LFC results
res_shrunken <- lfcShrink(
dds_full,
coef = str_c('treatment_', condition, '_vs_', control),
type = 'apeglm'
)
res_shrunken
## log2 fold change (MAP): treatment A vs C
## Wald test p-value: treatment A vs C
## DataFrame with 14816 rows and 5 columns
## baseMean log2FoldChange lfcSE pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000098104 6.35502 0.01967164 0.206079 0.551705 NA
## ENSMUSG00000103922 3.00821 -0.00811498 0.205596 0.773515 NA
## ENSMUSG00000033845 127.62317 -0.04685436 0.201406 0.491384 0.910778
## ENSMUSG00000102275 2.53432 0.00104597 0.205357 0.972052 NA
## ENSMUSG00000025903 96.02443 -0.05793424 0.187805 0.526801 0.914774
## ... ... ... ... ... ...
## ENSMUSG00000061654 74.15062 -0.00862303 0.207574 NA NA
## ENSMUSG00000079834 36.06458 -0.03367781 0.198685 0.60930718 0.925945
## ENSMUSG00000095041 247.75852 -0.00209616 0.198841 0.96470305 0.995670
## ENSMUSG00000063897 25.05491 0.01339576 0.194495 0.84692669 0.977817
## ENSMUSG00000095742 5.67615 2.01391517 1.919874 0.00521097 NA
plotMA(res_shrunken)
mcols(res_shrunken)
## DataFrame with 5 rows and 2 columns
## type description
## <character> <character>
## baseMean intermediate mean of normalized c..
## log2FoldChange results log2 fold change (MA..
## lfcSE results posterior SD: treatm..
## pvalue results Wald test p-value: t..
## padj results BH adjusted p-values
summary(res_shrunken, alpha = pval_cutoff)
##
## out of 14816 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 7, 0.047%
## LFC < 0 (down) : 60, 0.4%
## outliers [1] : 159, 1.1%
## low counts [2] : 3159, 21%
## (mean count < 6)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
Summary details
# Upregulated genes (LFC > 0)
res_shrunken_sig_df %>% filter(log2FoldChange > 0)
# Downregulated genes (LFC < 0)
res_shrunken_sig_df %>% filter(log2FoldChange < 0)
# Outliers (pvalue and padj are NA)
res_shrunken[which(is.na(res_shrunken$pvalue)), ]
## log2 fold change (MAP): treatment A vs C
## Wald test p-value: treatment A vs C
## DataFrame with 159 rows and 5 columns
## baseMean log2FoldChange lfcSE pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000103509 7.4013 -0.0123157 0.207854 NA NA
## ENSMUSG00000079554 27.6269 -0.0114825 0.207787 NA NA
## ENSMUSG00000085842 28.7558 0.0246738 0.209305 NA NA
## ENSMUSG00000103553 11.1104 -0.0177780 0.208382 NA NA
## ENSMUSG00000047496 20.2028 -0.0492316 0.214096 NA NA
## ... ... ... ... ... ...
## ENSMUSG00000036452 108.1271 -0.06071289 0.220435 NA NA
## ENSMUSG00000053846 27.1020 -0.03057686 0.210614 NA NA
## ENSMUSG00000024867 18.2290 -0.02871114 0.208606 NA NA
## ENSMUSG00000048029 22.7289 -0.00870315 0.207584 NA NA
## ENSMUSG00000061654 74.1506 -0.00862303 0.207574 NA NA
# Low counts (only padj is NA)
res_shrunken[which(is.na(res_shrunken$padj) & !is.na(res_shrunken$pvalue)), ]
## log2 fold change (MAP): treatment A vs C
## Wald test p-value: treatment A vs C
## DataFrame with 3159 rows and 5 columns
## baseMean log2FoldChange lfcSE pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000098104 6.35502 0.01967164 0.206079 0.551705 NA
## ENSMUSG00000103922 3.00821 -0.00811498 0.205596 0.773515 NA
## ENSMUSG00000102275 2.53432 0.00104597 0.205357 0.972052 NA
## ENSMUSG00000103280 2.25921 -0.00361950 0.205495 0.895140 NA
## ENSMUSG00000103355 3.20134 -0.00120582 0.204580 0.969475 NA
## ... ... ... ... ... ...
## ENSMUSG00000065947 5.53967 0.0697355 0.221195 0.07681665 NA
## ENSMUSG00000064364 4.38183 -0.0419282 0.210623 0.22773891 NA
## ENSMUSG00000064365 2.11846 0.0237202 0.208145 0.28517145 NA
## ENSMUSG00000064366 4.04585 -0.0114088 0.206890 0.55578956 NA
## ENSMUSG00000095742 5.67615 2.0139152 1.919874 0.00521097 NA
Visualizing results
Heatmaps
# Plot normalized counts (z-scores)
pheatmap(counts_sig_norm[2:7],
color = brewer.pal(8, 'YlOrRd'),
cluster_rows = T,
show_rownames = F,
annotation_col = as.data.frame(colData(dds)) %>% select(label),
border_color = NA,
fontsize = 10,
scale = 'row',
fontsize_row = 10,
height = 20)
# Plot log-transformed counts
pheatmap(counts_sig_log[2:7],
color = rev(brewer.pal(8, 'RdYlBu')),
cluster_rows = T,
show_rownames = F,
annotation_col = as.data.frame(colData(dds)) %>% select(label),
border_color = NA,
fontsize = 10,
fontsize_row = 10,
height = 20)
# Plot log-transformed counts (top 24 DE genes)
pheatmap(counts_sig_log %>% filter(ensembl_gene_id %in% (res_sig_df %>% head(24))$ensembl_gene_id) %>% select(-ensembl_gene_id) %>% column_to_rownames(var = 'mgi_symbol'),
color = rev(brewer.pal(8, 'RdYlBu')),
cluster_rows = T,
show_rownames = T,
annotation_col = as.data.frame(colData(dds)) %>% select(label),
fontsize = 10,
fontsize_row = 10,
height = 20)
Volcano plots
# Unshrunken LFC
res_df %>%
mutate(
sig_threshold = if_else(
padj < pval_cutoff & abs(log2FoldChange) >= lfc_cutoff,
if_else(log2FoldChange > 0, 'DE-up', 'DE-down'),
'non-DE'
)
) %>%
filter(!is.na(sig_threshold)) %>%
ggplot() +
geom_point(aes(x = log2FoldChange, y = -log10(padj), colour = sig_threshold)) +
scale_color_manual(values = c('blue', 'red', 'gray')) +
xlab('log2 fold change') +
ylab('-log10 adjusted p-value')
# Shrunken LFC
res_shrunken_df %>%
mutate(
sig_threshold = if_else(
padj < pval_cutoff & abs(log2FoldChange) >= lfc_cutoff,
if_else(log2FoldChange > 0, 'DE-up', 'DE-down'),
'non-DE'
)
) %>%
filter(!is.na(sig_threshold)) %>%
ggplot() +
geom_point(aes(x = log2FoldChange, y = -log10(padj), colour = sig_threshold)) +
scale_color_manual(values = c('blue', 'red', 'gray')) +
xlab('log2 fold change') +
ylab('-log10 adjusted p-value')
GSEA (all)
Hallmark genesets
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_h) %>% plot_enrichment_table(rank_lfc, mm_h)
# Wald stat
get_fgsea_res(rank_stat, mm_h) %>% plot_enrichment_table(rank_stat, mm_h)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_h) %>% plot_enrichment_table(rank_pval, mm_h)
GO biological process
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_bp) %>% plot_enrichment_table(rank_lfc, mm_c5_bp)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_bp) %>% plot_enrichment_table(rank_stat, mm_c5_bp)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_bp) %>% plot_enrichment_table(rank_pval, mm_c5_bp)
GO cellular component
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_cc) %>% plot_enrichment_table(rank_lfc, mm_c5_cc)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_cc) %>% plot_enrichment_table(rank_stat, mm_c5_cc)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_cc) %>% plot_enrichment_table(rank_pval, mm_c5_cc)
GO molecular function
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_mf) %>% plot_enrichment_table(rank_lfc, mm_c5_mf)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_mf) %>% plot_enrichment_table(rank_stat, mm_c5_mf)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_mf) %>% plot_enrichment_table(rank_pval, mm_c5_mf)
GSEA (DE)
Hallmark genesets
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_h) %>% plot_enrichment_table(rank_lfc, mm_h)
# Wald stat
get_fgsea_res(rank_stat, mm_h) %>% plot_enrichment_table(rank_stat, mm_h)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_h) %>% plot_enrichment_table(rank_pval, mm_h)
GO biological process
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_bp) %>% plot_enrichment_table(rank_lfc, mm_c5_bp)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_bp) %>% plot_enrichment_table(rank_stat, mm_c5_bp)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_bp) %>% plot_enrichment_table(rank_pval, mm_c5_bp)
GO cellular component
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_cc) %>% plot_enrichment_table(rank_lfc, mm_c5_cc)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_cc) %>% plot_enrichment_table(rank_stat, mm_c5_cc)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_cc) %>% plot_enrichment_table(rank_pval, mm_c5_cc)
GO molecular function
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_mf) %>% plot_enrichment_table(rank_lfc, mm_c5_mf)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_mf) %>% plot_enrichment_table(rank_stat, mm_c5_mf)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_mf) %>% plot_enrichment_table(rank_pval, mm_c5_mf)
System info
sessionInfo()
## R version 4.1.0 (2021-05-18)
## Platform: x86_64-conda-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
##
## Matrix products: default
## BLAS/LAPACK: /home/chan/mRNA_seq_pipeline/.snakemake/conda/9a19315a020c824d12f8055f7c009b0f/lib/libopenblasp-r0.3.18.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] fgsea_1.20.0 RColorBrewer_1.1-2 pheatmap_1.0.12 DESeq2_1.34.0 SummarizedExperiment_1.24.0 Biobase_2.54.0 MatrixGenerics_1.6.0 matrixStats_0.61.0 GenomicRanges_1.46.0 GenomeInfoDb_1.30.0 IRanges_2.28.0 S4Vectors_0.32.0 BiocGenerics_0.40.0 scales_1.1.1 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4 readr_2.1.1 tidyr_1.1.4 tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1
##
## loaded via a namespace (and not attached):
## [1] colorspace_2.0-2 ellipsis_0.3.2 XVector_0.34.0 fs_1.5.1 rstudioapi_0.13 farver_2.1.0 bit64_4.0.5 mvtnorm_1.1-3 AnnotationDbi_1.56.1 fansi_0.4.2 apeglm_1.16.0 lubridate_1.8.0 xml2_1.3.3 splines_4.1.0 cachem_1.0.6 geneplotter_1.72.0 knitr_1.35 jsonlite_1.7.2 broom_0.7.10 annotate_1.72.0 dbplyr_2.1.1 png_0.1-7 compiler_4.1.0 httr_1.4.2 backports_1.4.0 assertthat_0.2.1 Matrix_1.3-4 fastmap_1.1.0 cli_3.1.0 htmltools_0.5.2 tools_4.1.0 coda_0.19-4 gtable_0.3.0 glue_1.5.1 GenomeInfoDbData_1.2.7 fastmatch_1.1-3 Rcpp_1.0.7 bbmle_1.0.24 cellranger_1.1.0 jquerylib_0.1.4 vctrs_0.3.8 Biostrings_2.62.0 xfun_0.28 rvest_1.0.2 lifecycle_1.0.1 XML_3.99-0.8 MASS_7.3-54 zlibbioc_1.40.0 vroom_1.5.7 hms_1.1.1 parallel_4.1.0 yaml_2.2.1 memoise_2.0.1 gridExtra_2.3 emdbook_1.3.12 bdsmatrix_1.3-4 stringi_1.7.6 RSQLite_2.2.8 highr_0.9 genefilter_1.76.0 BiocParallel_1.28.0 rlang_0.4.12 pkgconfig_2.0.3 bitops_1.0-7 evaluate_0.14 lattice_0.20-45 labeling_0.4.2 bit_4.0.4 tidyselect_1.1.1 plyr_1.8.6 magrittr_2.0.1 R6_2.5.1 generics_0.1.1 DelayedArray_0.20.0 DBI_1.1.1 pillar_1.6.4 haven_2.4.3 withr_2.4.3 survival_3.2-13 KEGGREST_1.34.0 RCurl_1.98-1.5 modelr_0.1.8 crayon_1.4.2 utf8_1.2.2 tzdb_0.2.0 rmarkdown_2.11 locfit_1.5-9.4 grid_4.1.0 readxl_1.3.1 data.table_1.14.2 blob_1.2.2 reprex_2.0.1 digest_0.6.29 xtable_1.8-4 numDeriv_2016.8-1.1 munsell_0.5.0